import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification


def 	lowerCamelCase					(	UpperCamelCase     :				Union[str, Any]							)   ->    Tuple:
		_lowerCamelCase				   =  []
		embed.append(
		    (
		        F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
		        F"""stage{idx}.patch_embed.proj.weight""",
		    )							)
		embed.append(
		    (
		        F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
		        F"""stage{idx}.patch_embed.proj.bias""",
		    )							)
		embed.append(
		    (
		        F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
		        F"""stage{idx}.patch_embed.norm.weight""",
		    )							)
		embed.append(
		    (
		        F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
		        F"""stage{idx}.patch_embed.norm.bias""",
		    )							)
		return embed


def 	lowerCamelCase					(	UpperCamelCase     :				Dict			,       UpperCamelCase     :				List[Any]							)   ->    Dict:
		_lowerCamelCase				   =  []
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
		        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
		        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
		        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
		        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
		        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
		        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
		        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
		        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
		        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
		        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
		        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
		        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
		        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
		        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
		        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
		        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
		        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
		        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
		        F"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
		        F"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
		        F"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
		        F"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
		        F"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
		        F"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
		        F"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
		    )							)
		attention_weights.append(
		    (
		        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
		        F"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
		    )							)
		attention_weights.append(
		    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""")							)
		attention_weights.append(
		    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""")							)
		attention_weights.append(
		    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""")							)
		attention_weights.append(
		    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""")							)
		attention_weights.append(
		    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", F"""stage{idx}.blocks.{cnt}.norm1.weight""")							)
		attention_weights.append(
		    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", F"""stage{idx}.blocks.{cnt}.norm1.bias""")							)
		attention_weights.append(
		    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", F"""stage{idx}.blocks.{cnt}.norm2.weight""")							)
		attention_weights.append(
		    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", F"""stage{idx}.blocks.{cnt}.norm2.bias""")							)
		return attention_weights


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple							)   ->    Optional[int]:
		_lowerCamelCase				   =  []
		token.append((F"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token')							)
		return token


def 	lowerCamelCase					(	)   ->    Optional[int]:
		_lowerCamelCase				   =  []
		head.append(('layernorm.weight', 'norm.weight')							)
		head.append(('layernorm.bias', 'norm.bias')							)
		head.append(('classifier.weight', 'head.weight')							)
		head.append(('classifier.bias', 'head.bias')							)
		return head


def 	lowerCamelCase					(	UpperCamelCase     :				Union[str, Any]			,       UpperCamelCase     :				int			,       UpperCamelCase     :				Optional[Any]			,       UpperCamelCase     :				int							)   ->    List[str]:
		_lowerCamelCase				   =  'imagenet-1k-id2label.json'
		_lowerCamelCase				   =  10_00

		_lowerCamelCase				   =  'huggingface/label-files'
		_lowerCamelCase				   =  num_labels
		_lowerCamelCase				   =  json.load(open(cached_download(hf_hub_url(UpperCamelCase			,       UpperCamelCase			,       repo_type='dataset'							)							)			,       'r'							)							)
		_lowerCamelCase				   =  {int(UpperCamelCase							): v for k, v in idalabel.items()}

		_lowerCamelCase				   =  idalabel
		_lowerCamelCase				   =  {v: k for k, v in idalabel.items()}

		_lowerCamelCase				   =  _lowerCamelCase				   =  CvtConfig(num_labels=UpperCamelCase			,       idalabel=UpperCamelCase			,       labelaid=UpperCamelCase							)

		# For depth size 13 (13 = 1+2+10)
		if cvt_model.rsplit('/'			,       1							)[-1][4:6] == "13":
				_lowerCamelCase				   =  [1, 2, 10]

		# For depth size 21 (21 = 1+4+16)
		elif cvt_model.rsplit('/'			,       1							)[-1][4:6] == "21":
				_lowerCamelCase				   =  [1, 4, 16]

		# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
		else:
				_lowerCamelCase				   =  [2, 2, 20]
				_lowerCamelCase				   =  [3, 12, 16]
				_lowerCamelCase				   =  [1_92, 7_68, 10_24]

		_lowerCamelCase				   =  CvtForImageClassification(UpperCamelCase							)
		_lowerCamelCase				   =  AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'							)
		_lowerCamelCase				   =  image_size
		_lowerCamelCase				   =  torch.load(UpperCamelCase			,       map_location=torch.device('cpu'							)							)

		_lowerCamelCase				   =  OrderedDict()
		_lowerCamelCase				   =  []

		for idx in range(len(config.depth							)							):
				if config.cls_token[idx]:
						_lowerCamelCase				   =  list_of_state_dict + cls_token(UpperCamelCase							)
				_lowerCamelCase				   =  list_of_state_dict + embeddings(UpperCamelCase							)
				for cnt in range(config.depth[idx]							):
						_lowerCamelCase				   =  list_of_state_dict + attention(UpperCamelCase			,       UpperCamelCase							)

		_lowerCamelCase				   =  list_of_state_dict + final()
		for gg in list_of_state_dict:
				print(UpperCamelCase							)
		for i in range(len(UpperCamelCase							)							):
				_lowerCamelCase				   =  original_weights[list_of_state_dict[i][1]]

		model.load_state_dict(UpperCamelCase							)
		model.save_pretrained(UpperCamelCase							)
		image_processor.save_pretrained(UpperCamelCase							)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
				A						=			argparse.ArgumentParser()
				parser.add_argument(
				    '--cvt_model',
				    default='cvt-w24',
				    type=str,
				    help='Name of the cvt model you\'d like to convert.',
				)
				parser.add_argument(
				    '--image_size',
				    default=3_8_4,
				    type=int,
				    help='Input Image Size',
				)
				parser.add_argument(
				    '--cvt_file_name',
				    default=r'cvtmodels\CvT-w24-384x384-IN-22k.pth',
				    type=str,
				    help='Input Image Size',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
				)

				A						=			parser.parse_args()
				convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


A						=			logging.get_logger(__name__)





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   ['pixel_values']
		def __init__(       self     :     List[str]     ,  snake_case__     :     bool = True     ,  snake_case__     :     Dict[str, int] = None     ,  snake_case__     :     PILImageResampling = PILImageResampling.BICUBIC     ,  snake_case__     :     bool = True     ,  snake_case__     :     Union[int, float] = 1 / 2_5_5     ,  snake_case__     :     bool = True     ,  snake_case__     :     Optional[Union[float, List[float]]] = None     ,  snake_case__     :     Optional[Union[float, List[float]]] = None     ,  snake_case__     :     bool = True     ,  **snake_case__     :     List[Any]     ,  )      ->	None:
				super().__init__(**snake_case__		)
				_lowerCamelCase				   =  size if size is not None else {'height': 3_8_4, 'width': 3_8_4}
				_lowerCamelCase				   =  get_size_dict(snake_case__     ,  default_to_square=snake_case__		)

				_lowerCamelCase				   =  do_resize
				_lowerCamelCase				   =  size
				_lowerCamelCase				   =  resample
				_lowerCamelCase				   =  do_rescale
				_lowerCamelCase				   =  rescale_factor
				_lowerCamelCase				   =  do_normalize
				_lowerCamelCase				   =  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
				_lowerCamelCase				   =  image_std if image_std is not None else OPENAI_CLIP_STD
				_lowerCamelCase				   =  do_convert_rgb
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     np.ndarray     ,  snake_case__     :     Dict[str, int]     ,  snake_case__     :     PILImageResampling = PILImageResampling.BICUBIC     ,  snake_case__     :     Optional[Union[str, ChannelDimension]] = None     ,  **snake_case__     :     Dict     ,  )      ->	np.ndarray:
				_lowerCamelCase				   =  get_size_dict(snake_case__     ,  default_to_square=snake_case__		)
				if "height" not in size or "width" not in size:
						raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""		)
				_lowerCamelCase				   =  (size['height'], size['width'])
				return resize(snake_case__     ,  size=snake_case__     ,  resample=snake_case__     ,  data_format=snake_case__     ,  **snake_case__		)
		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     np.ndarray     ,  snake_case__     :     Union[int, float]     ,  snake_case__     :     Optional[Union[str, ChannelDimension]] = None     ,  **snake_case__     :     Optional[int]     ,  )      ->	int:
				return rescale(snake_case__     ,  scale=snake_case__     ,  data_format=snake_case__     ,  **snake_case__		)
		def   _snake_case       (       self     :     Optional[Any]     ,  snake_case__     :     np.ndarray     ,  snake_case__     :     Union[float, List[float]]     ,  snake_case__     :     Union[float, List[float]]     ,  snake_case__     :     Optional[Union[str, ChannelDimension]] = None     ,  **snake_case__     :     Optional[int]     ,  )      ->	np.ndarray:
				return normalize(snake_case__     ,  mean=snake_case__     ,  std=snake_case__     ,  data_format=snake_case__     ,  **snake_case__		)



		def   _snake_case       (       self     :     List[str]     ,  snake_case__     :     ImageInput     ,  snake_case__     :     Optional[bool] = None     ,  snake_case__     :     Optional[Dict[str, int]] = None     ,  snake_case__     :     PILImageResampling = None     ,  snake_case__     :     Optional[bool] = None     ,  snake_case__     :     Optional[float] = None     ,  snake_case__     :     Optional[bool] = None     ,  snake_case__     :     Optional[Union[float, List[float]]] = None     ,  snake_case__     :     Optional[Union[float, List[float]]] = None     ,  snake_case__     :     Optional[Union[str, TensorType]] = None     ,  snake_case__     :     bool = None     ,  snake_case__     :     ChannelDimension = ChannelDimension.FIRST     ,  **snake_case__     :     str     ,  )      ->	PIL.Image.Image:
				_lowerCamelCase				   =  do_resize if do_resize is not None else self.do_resize
				_lowerCamelCase				   =  resample if resample is not None else self.resample
				_lowerCamelCase				   =  do_rescale if do_rescale is not None else self.do_rescale
				_lowerCamelCase				   =  rescale_factor if rescale_factor is not None else self.rescale_factor
				_lowerCamelCase				   =  do_normalize if do_normalize is not None else self.do_normalize
				_lowerCamelCase				   =  image_mean if image_mean is not None else self.image_mean
				_lowerCamelCase				   =  image_std if image_std is not None else self.image_std
				_lowerCamelCase				   =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

				_lowerCamelCase				   =  size if size is not None else self.size
				_lowerCamelCase				   =  get_size_dict(snake_case__     ,  default_to_square=snake_case__		)

				_lowerCamelCase				   =  make_list_of_images(snake_case__		)

				if not valid_images(snake_case__		):
						raise ValueError(
						    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
						    'torch.Tensor, tf.Tensor or jax.ndarray.'		)

				if do_resize and size is None or resample is None:
						raise ValueError('Size and resample must be specified if do_resize is True.'		)

				if do_rescale and rescale_factor is None:
						raise ValueError('Rescale factor must be specified if do_rescale is True.'		)

				if do_normalize and (image_mean is None or image_std is None):
						raise ValueError('Image mean and std must be specified if do_normalize is True.'		)

				# PIL RGBA images are converted to RGB
				if do_convert_rgb:
						_lowerCamelCase				   =  [convert_to_rgb(snake_case__		) for image in images]

				# All transformations expect numpy arrays.
				_lowerCamelCase				   =  [to_numpy_array(snake_case__		) for image in images]

				if do_resize:
						_lowerCamelCase				   =  [self.resize(image=snake_case__     ,  size=snake_case__     ,  resample=snake_case__		) for image in images]

				if do_rescale:
						_lowerCamelCase				   =  [self.rescale(image=snake_case__     ,  scale=snake_case__		) for image in images]

				if do_normalize:
						_lowerCamelCase				   =  [self.normalize(image=snake_case__     ,  mean=snake_case__     ,  std=snake_case__		) for image in images]

				_lowerCamelCase				   =  [to_channel_dimension_format(snake_case__     ,  snake_case__		) for image in images]

				_lowerCamelCase				   =  BatchFeature(data={'pixel_values': images}     ,  tensor_type=snake_case__		)

				return encoded_outputs